01379nas a2200097   4500000000100000008004100001100001500042245009000057856007200147520106200219       2026                            d1 aMert Nakip00aA predictive neural network architecture for early detection of low-rate cyberattacks  uhttps://www.sciencedirect.com/science/article/pii/S09507051260072153 a<p>Low-Rate Denial of Service (LDoS) attacks pose a significant challenge to IoT networks due to their subtle and prolonged nature, often evading traditional intrusion detection systems. This paper presents IDQS (Intrusion Detection via QoS Prediction), a lightweight and proactive framework for early LDoS attack detection. IDQS integrates two new key components: (i) RTP-QoS, a Recurrent Trend Predictive Neural Network that learns and forecasts future Quality of Service (QoS) based on historical traffic patterns, and (ii) PDM, a Pairwise Decision Model that evaluates discrepancies between predicted and actual QoS to identify potential attacks. Evaluated on the public SDN-SlowRate-DDoS and CIC-IDS2017 datasets, IDQS respectively achieves over 79% and 91% detection accuracy across most attack scenarios with high recall and low false negatives, while maintaining an end-to-end inference time of just 0.28 seconds. The results demonstrate the effectiveness and efficiency of IDQS for real-time deployment in resource-constrained IoT environments.</p>